Data Analytics for Beginners

Data analytics is a strategy-based science where raw data is analyzed to detect trends, answer questions, or draw conclusions from a large batch of data. Using various techniques, raw data is converted into a form that allows companies and organizations to analyze important metrics. Without data analytics, these metrics would likely be buried under a mass of information. This process helps companies increase their overall efficiency.  

Pursuing a career as a data analyst is a great choice if you're willing to keep learning and challenging yourself. In exchange, you'll enjoy a growing field of job prospects, as well as a lasting, high income career. For those of you who are ready to start your career in data analysis, here are the skills you'll need to learn, and our tips for landing your first job.

Types of Data Analysis

Based on the techniques used, data analysis can be classified into the following categories:

• Descriptive Analysis: This kind of analysis is used to analyze and describe the features of data. Past data is what is usually used to draw conclusions. The two major techniques used in this type of analysis are data aggregation and data mining. The data is gathered first, and then it is sorted through data aggregation. Next, patterns and meanings are identified through data mining.
• Predictive Analysis: As the name suggests, this type of analysis is used to predict future outcomes. Based on past or historical data, a set of likely outcomes for the future is determined. Descriptive analysis is also a part of this process, as it is used to generate the predictions. In this kind of analysis, large data is required which is mined skillfully using predictive models. This ensures that accurate predictions can be made.
• Diagnostic Analysis: Sometimes, there can be mistakes within the data. These anomalies can negatively affect the performance of a business. The purpose of diagnostic analysis is to diagnose these problems and errors. The performance and strategies of a business can be improved if the issues are diagnosed and fixed.
• Prescriptive Analysis: This is a combination of all the other types of analysis. Instead of focusing on data monitoring, this technique emphasizes actionable insights. The data is gathered using both descriptive and predictive models, as well as a combination of math and computer science.

Process of Analyzing Data

The following are the steps involved in the data analysis process.  

1. Data Requirement: First of all, you need to decide what kind of data you require. For instance, you might need the population of a particular area or the average income of a certain group of people.
2. Data Collection: Data is collected whenever it’s necessary. Accuracy is a crucial part of collecting data because if it is not accurate, then the conclusion drawn will be wrong.
3. Data Processing: This means that the gathered data has to be analyzed or organized. For example, you might choose to arrange the data in rows and columns in a spreadsheet.
4. Data Cleaning: Not all of the data collected will be useful. Also, there are likely some errors in the data. At this stage, the data is cleaned and the errors are either removed or fixed. This step is important before analysis so that the outcome is accurate.
5. Data Analysis: At this step, the data is analyzed and the conclusions are made. Various data analysis tools and techniques are used like data visualization, correlation, and regression analysis. Sometimes, there may still be some errors left even after data cleaning; these errors should be removed in this step.
6. Communication: After analyzing the data, it is converted into an organized, simplified document. This document is used to take insightful actions and make data-backed decisions. For example, it might be presented in the form of charts, tables, or graphs.

Skills Required To Become A Data Analyst

Some of the skills required to become a data analyst are as follows.

• Data Visualization: As a data analyst, data visualization is a necessary and engaging way of presenting data. You should know how to create charts and tables so that clients or an audience can understand the data easily. This skill will help you progress in your career.
• Machine Learning: Machine learning is considered an important skill because it is used in artificial intelligence and predictive analysis. You might not be an expert in machine learning, but you should at least know the basics.
• Statistical Knowledge: Probability and statistics have become the key aspects of analyzing data. If your statistical knowledge is good, then you will not make errors in arranging, analyzing, and interpreting data.
• Data Cleaning Skills: Acquiring this skill is a must if you want to become a data analyst. You should know how to find inconsistencies, errors, and anomalies in data.
• Microsoft Excel: Along with having a basic understanding of excel, you should know about advanced excel methods like VBA lookups and writing Macros.
• SQL: SQL stands for Structured Query Language, which is an important skill for a data analyst. This will help you extract data from various sources.

Tips for Becoming A Data Analyst

Choose The Right Education: To become a data analyst you’ll need to have some formal training on the subject. These days, there are many certification courses available, both online and in-person. Thinkful’s Data Analytics programs are in-depth and designed to get you a job after you graduate.

1. Get Experience: Most jobs will require some experience in the field. You can gain an edge over the competition by working as an intern first. The hands-on problem solving and data analysis experience you gain through an internship will be invaluable, even later on in your career. Another great way to gain valuable insight into this field is by working with a mentor. Thinkful’s programs provide you with 1-on-1 mentorship to set you up for success. Apart from internships and mentors, you can work on small projects. Small projects will reflect well on your portfolio.
2. Land A job: Initially, your job profile may be that of a junior data analyst or an entry-level data analyst. With these jobs, you can hone your data analyzing skills, which will help you progress in your career.
3. Keep Learning: Getting a job doesn't mean you've reached the end of your learning journey. You should always be eager to learn about new techniques, innovative tools, and strategies. If you want to move ahead in your career, then you can also take up some additional certification courses like Microsoft Certified Solutions Expert (MCSE). Continuing education will not only be good for gaining more knowledge in your field, but you may also see it reflected in your salary.

If you're motivated and passionate about data, then becoming a data analyst may be a great career option for you.  If you want to read more about data analytics, Thinkful’s blog is full of informative data analytics articles for beginners.